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© Automated cell tracking in a Parhyale hawaiensis embryo. Wolff et al., 2018.
Publication : F1000Research

Automated cell tracking using StarDist and TrackMate.

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in F1000Research - 01 Jan 2020

Fazeli E, Roy NH, Follain G, Laine RF, von Chamier L, Hänninen PE, Eriksson JE, Tinevez JY, Jacquemet G,

Link to Pubmed [PMID] – 33224481

Link to DOI – 10.12688/f1000research.27019.1

F1000Res 2020 ; 9(): 1279

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline’s usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

https://pubmed.ncbi.nlm.nih.gov/33224481